AUTHOR=Wang Shuwang , Liu Feng , Yin Xin-xin , Chen Kerui , Cai Run TITLE=Employing convolution-enhanced attention mechanisms for earthquake detection and phase picking models JOURNAL=Frontiers in Earth Science VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1283857 DOI=10.3389/feart.2023.1283857 ISSN=2296-6463 ABSTRACT=

In response to the challenge of improving the performance of deep learning models for earthquake detection in low signal-to-noise ratio environments, this article introduces a new earthquake detection model called ECPickNet. Drawing inspiration from the EQTransformer, this model leverages Convolution-Enhanced Transformer technology, Conformer architecture, and incorporates the Residual Stacking Block Unit with Channel-Skipping (RSBU-CS) module. The manuscript provides a detailed overview of the model’s network architecture, parameter settings used during the training process, and compares it with several similar methods through a series of experiments. The experimental results highlight ECPickNet’s well performance on both the STEAD and Gansu datasets, particularly performing exceptionally well in the processing of low signal-to-noise ratio data. Interested readers can access and download the proposed method from the following website address: https://github.com/20041170036/EcPick.